Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

By Leigh R. Hochberg, D. Bacher, B. Jarosiewicz, Nicolas Y. Masse, John D. Simeral, Joern Vogel, Sami Haddadin, Jie Liu, Sydney S. Cash, Patrick van der Smagt and 1 more. Nature, 2012

2,559 citations recorded by the source corpus.

Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices. Able-bodied monkeys have used a neural interface system to control a robotic arm, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/086aafcc1f96bb299806e1a3b1ee56442c2bc47c

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Citations in the corpus

Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface (2015, background): “With additional constraints on the assumptions of the intended movements, similar approaches could also conceivably be extended to multidimensional neural control, such as prosthetic and robotic arm reach and grasp (7, 8) or functional electrical stimulation of the person’s own limbs (33–35).”
Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019, background): “The results presented here demonstrate that CP control provides a unique opportunity for the complex control of a virtual cursor and robotic device (14, 39), without requiring discretized, prolonged task sequences (46) that can make even simple task completion long and frustrating.”
High performance communication by people with paralysis using an intracortical brain-computer interface (2017, methodology): “…studies of intracortical BCIs have either used generalized (task-independent) measures of performance ( Simeral et al., 2011 ; Gilja et al., 2015 ) or application-focused (task-dependent) measures ( Bacher et al., 2015 ; Jarosiewicz et al., 2015 ; Hochberg et al., 2012 ; Collinger et al., 2013a ).”
Cortical control of arm movements: a dynamical systems perspective. (2013, background): “We review how a dynamical systems perspective may help us understand why neural activity evolves the way it does, how neural activity relates to movement parameters, and how a unified conceptual framework may result.”
Progress Towards Biocompatible Intracortical Microelectrodes for Neural Interfacing Applications (2014, background): “(6) For example, one patient was able to use her thoughts to control a robotic arm, reach and grasp a bottle of coffee, bring it towards her mouth to drink, and then return the bottle to the table for the first time in 14 years.”
Eye motion triggered self-powered mechnosensational communication system using triboelectric nanogenerator (2017, background): “To date, the bioelectrical signals applied to HMIs include neuron signals (1, 2, 5), as well as electrocorticogram (6), electroencephalogram (EEG) (3), electromyogram (EMG) (7), electrooculogram (EOG) signals (8–10), etc.”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, methodology): “These decoding achievements for upper limb kinematics using invasive electrodes in monkeys and humans have resulted in accurate control of prosthetic devices in 3D space [13 – 17].”
Teleoperation with intelligent and customizable interfaces (2013, background): “, 2011), to customizable brain interfaces (Hochberg, 2012).”
Control strategies for active lower extremity prosthetics and orthotics: a review (2015, positive): “Intracortical electrode arrays have been successfully demonstrated to allow control of multi-degree-of-freedom reach and grasp movements with robotic arms in tetraplegic subjects [55,56],”
Progress in Neuroengineering for brain repair: New challenges and open issues (2018, background): “, 1999), BMIs have become increasingly sophisticated and have proved to be successfully applicable to paralysed humans (Ajiboye et al., 2017; Bouton et al., 2016; Hochberg et al., 2012).”